Temperature drives ecosystem function and can be estimated from potential solar irradiation (the surface angle of the Earth in relationship to the sun). However, indirect topographical proxies (i.e., aspect) are often used in lieu of direct temperature estimates; and temperature models based on potential solar irradiation typically omit elevation, a key parameter in temperature estimation. Using temperature data (2002–04) from regional weather stations and field sites in the southern Appalachian region of North Carolina and north Georgia, I test the efficacy of temperature estimations based on potential solar irradiation and present a simple method to improve such estimations by incorporating elevational temperature gradients. The heat load index, which weighs afternoon sun as more integral to heat generation than morning sun, fits actual weather station and field site temperatures better than solar angle alone. However, in all years and data sets, adjusting the heat load index for elevational substantially improves its fit with actual temperatures. Further, by calibrating the adjusted heat load estimation with annual weather station temperature data, actual field site temperature for individual years can be accurately predicted. This paper presents a relatively simple method for generating temperature data that requires only spreadsheet or statistical software. This is useful for estimating temperatures from data sets where only topographical or GPS data were collected. It also can be used to derive missing data when sampling sites outnumber temperature loggers.